ReAd: reactive-adaptive methodology to enable evolving intelligent agents for virtual environments

Abstract

This paper describes our novel reactive-adaptive methodology (ReAd) for the creation of Intelligent Agents capable of evolving to self-develop, in virtual environments. We start with AI concepts, which are well established for the implementation of character behaviour in serious games, such as Fuzzy Logic, the Belief-Desire-Intention model (BDI), and Finite State Machines (FSM); and discuss their characteristics. In particular for BDI and FSM, we analyse their limitations for being manipulated at run-time, which in turn limits their use in evolvable systems. We present a novel combination of these techniques, based on a Rational-Reactive structure (RaRe) to optimize their performance and enable the process of online self-adaptation so that they can be used to create evolving intelligent agents. The focus of the work is in enabling a structure to be evolvable; the detail of the adaptation process itself is not in the critical domain of this paper. We present an analysis of our system in a test scenario, where the standard implementation is compared to our novel ReAd methodology.